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First, success would mean achieving a shared understanding of core AI governance principles including transparency, accountability, fairness, safety, and respect for human rights. While complete consensus may be unrealistic, aligning on baseline norms would provide a common direction for future policy making. Second, the Dialogue should produce a clear road map for international cooperation, outlining how governments, industry, academia, and civil society can collaborate. This includes identifying priority areas such as risk management, data governance, and cross-border regulatory alignment, while avoiding fragmented or conflicting approaches. Third, a key outcome should be meaningful inclusion of developing countries. This involves concrete commitments toward capacity-building, knowledge sharing, and equitable access to AI technologies. Without such measures, global AI governance risks reinforcing existing inequalities. Fourth, the Dialogue should establish mechanisms for ongoing engagement, such as working groups, follow-up forums, or partnerships that ensure continuity beyond the initial meeting. Governance of AI is an evolving challenge, and sustained dialogue is essential. Finally, success would be reflected in action-oriented commitments, rather than purely declaratory statements. These could include pilot initiatives, voluntary guidelines, or collaborative projects that demonstrate how principles can be implemented in practice. In essence, the Dialogue would be successful if it moves beyond discussion to building trust, fostering inclusion, and initiating tangible steps toward a coordinated and responsible global AI governance framework.

• AI capacity-building
• Transparency, accountability, and human oversight
• Interoperability of governance approaches
• Social, economic, ethical, cultural, linguistic and technical implications of AI

AI capacity-building is equally critical to ensure that countries with limited resources are not left behind. Strengthening local expertise, infrastructure, and regulatory capabilities enables more equitable participation in the global AI ecosystem. The social, economic, ethical, cultural, linguistic, and technical implications of AI must be addressed to ensure that AI systems are context-sensitive and inclusive. This is particularly important for underrepresented languages and communities, where AI can either empower or marginalize. Interoperability of governance approaches is essential to avoid fragmented regulations across jurisdictions. Harmonized frameworks can facilitate innovation, cross-border collaboration, and responsible deployment of AI technologies. Transparency, accountability, and human oversight are necessary to build trust and ensure that AI systems remain explainable and subject to appropriate checks and balances. Together, these priorities emphasize a balanced approach-promoting innovation while safeguarding equity, rights, and global inclusivity in AI governance.

A key challenge lies in limited regulatory frameworks and institutional capacity. While AI adoption is increasing across sectors such as education, energy, and public services, governance mechanisms for safe, secure, and trustworthy AI remain underdeveloped. This raises risks related to data privacy, cybersecurity, and algorithmic bias, particularly where oversight and accountability structures are weak. Capacity constraints further widen the gap. There is a shortage of skilled professionals, research infrastructure, and localized datasets, which limits the ability to develop context-relevant AI solutions. As a result, many systems are imported and may not reflect local languages, cultural nuances, or socio-economic realities, reinforcing digital inequality. The lack of interoperability in governance approaches also affects cross-border collaboration. Diverging regulatory standards between regions complicate technology transfer, investment, and innovation, especially for emerging economies seeking to integrate into global AI value chains. At the same time, these gaps present significant opportunities. AI can accelerate progress in critical sectors such as energy efficiency, climate action, agriculture, and healthcare, where data-driven solutions can improve service delivery and resource management. With the right governance frameworks, AI can support sustainable development and economic growth. The growing global emphasis on open-source models and collaborative platforms also offers opportunities for knowledge sharing and innovation. By leveraging open ecosystems, countries with limited resources can build local solutions and strengthen digital sovereignty. Finally, there is increasing recognition of the need to embed human rights, transparency, and accountability into AI systems. This creates an opportunity to shape governance models that are inclusive, ethical, and responsive to local needs. Overall, addressing governance gaps is essential to ensure that AI becomes a tool for equitable development rather than a driver of further inequality.

At the international level, the OECD AI Principles provide a widely endorsed framework emphasizing human-centered values, transparency, robustness, and accountability. Similarly, the EU AI Act introduces a risk-based regulatory approach, categorizing AI systems by their potential harm and applying proportionate obligations-offering a strong example of enforceable governance. The UNESCO Recommendation on the Ethics of Artificial Intelligence is another key initiative, particularly relevant for developing countries, as it integrates human rights, inclusivity, and capacity-building into AI governance. On the technical side, algorithmic impact assessments (AIAs) are increasingly used by governments (e.g., Canada) to evaluate risks before deploying AI systems. These tools enhance transparency and accountability by requiring institutions to assess potential biases, ethical risks, and societal impacts. Open and collaborative approaches also play a critical role. Platforms like Hugging Face and TensorFlow promote open-source AI development, enabling researchers and developers-especially in resource-constrained settings-to access tools, datasets, and models. This supports innovation while encouraging peer review and transparency. In addition, national AI strategies (such as those adopted by countries like the UAE and Singapore) demonstrate how governments can align AI development with economic priorities, ethical standards, and capacity-building initiatives. Finally, multi-stakeholder governance platforms, such as global AI forums and public-private partnerships, foster dialogue, knowledge exchange, and policy alignment across borders. Together, these examples highlight that effective AI governance requires a combination of regulatory frameworks, technical tools, ethical guidelines, and open collaboration, ensuring that AI systems are both innovative and aligned with societal values.